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Article overview
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Long-Range Indoor Navigation with PRM-RL | Anthony Francis
; Aleksandra Faust
; Hao-Tien Lewis Chiang
; Jasmine Hsu
; J. Chase Kew
; Marek Fiser
; Tsang-Wei Edward Lee
; | Date: |
25 Feb 2019 | Abstract: | Long-range indoor navigation requires guiding robots with noisy sensors and
controls through cluttered environments along paths that span a variety of
buildings. We achieve this with PRM-RL, a hierarchical robot navigation method
in which reinforcement learning agents that map noisy sensors to robot controls
learn to solve short-range obstacle avoidance tasks, and then sampling-based
planners map where these agents can reliably navigate in simulation; these
roadmaps and agents are then deployed on-robot, guiding the robot along the
shortest path where the agents are likely to succeed. Here we use Probabilistic
Roadmaps (PRMs) as the sampling-based planner and AutoRL as the reinforcement
learning method in the indoor navigation context. We evaluate the method in
simulation for kinematic differential drive and kinodynamic car-like robots in
several environments, and on-robot for differential-drive robots at two
physical sites. Our results show PRM-RL with AutoRL is more successful than
several baselines, is robust to noise, and can guide robots over hundreds of
meters in the face of noise and obstacles in both simulation and on-robot,
including over 3.3 kilometers of physical robot navigation. | Source: | arXiv, 1902.9458 | Services: | Forum | Review | PDF | Favorites |
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